How IVF and HNSW Power Fast Vector Search in Modern AI Systems

Vector search underpins semantic AI applications like RAG pipelines, recommendation engines, and image search by finding the closest matching vectors among millions. Because high-dimensional embeddings suffer from the curse of dimensionality, exact nearest-neighbor methods break down and approximate nearest neighbor (ANN) search is used instead. ANN algorithms trade a correctness guarantee for speed, measuring quality through recall — the fraction of true top-k neighbors actually returned. Two algorithms, Inverted File Index (IVF) and Hierarchical Navigable Small World (HNSW), handle most of the work in popular libraries like pgvector, Qdrant, and FAISS. Understanding how each works under the hood helps engineers choose the right approach and tune the speed-versus-recall tradeoff for their use case.